Vol. 131 (2017)
No. 3
ACTA PHYSICA POLONICA A
Special Issue of the 6th International Congress & Exhibition (APMAS2016), Maslak, Istanbul, Turkey, June 1–3, 2016
Optimisation of Machining Parameters in Turning AISI 304L
Stainless Steel by the Grey-Based Taguchi Method
M. Ay∗
Marmara University, Faculty of Technology, Department of Mechanical Engineering, 34722, Istanbul, Turkey
In this study, an experimental optimization of cutting forces and surface roughness in turning of AISI
304L stainless steel using wiper and conventional insert cutting tools with dry cutting conditions are presented.
The influences of feed rate, depth of cut, and corner radius on surface roughness, cutting force and surface hardness are examined. In order to optimize the turning process, Grey relational analysis optimization method is used.
The optimal machinability conditions of AISI 304L stainless steel with coated carbide insert are successfully determined in this study.
DOI: 10.12693/APhysPolA.131.349
PACS/topics: 81.05.–t
1. Introduction
Machining has maintained its importance for years and
the research in this field has been closely followed by the
manufacturers. Every act of manufacturing has a cost
and there are some factors which determine these costs.
The cost of cutting tools and the cost of the workpiece
can be considered as the two important factors in question. Thus, to lower the manufacturing cost and buy
the product on cheap, those factors should be taken into
consideration.
For the cutting tools to be long lived and to prevent
the waste of the raw material, while producing the workpiece with the required level of quality, the need for the
optimization of the cutting performance and conditions
has arisen. To achieve that, the factors which affect the
life of the cutting tools and determine the quality of the
workpiece have been investigated by the scientists. The
research has revealed the fact that there are a number
of parameters and conditions in turning, which affect the
above-mentioned factors [1, 2]. Tool manufacturers are
continuously improving their materials, offering new coatings for cutting edges and modifying the geometry of
cutting inserts. These are geometric properties of the
cutting tool, tip angles, approach angle, feed, cutting
speed, depth of cut, coatings, cooling liquid, chip breaker form, workpiece, rigidity of the cutting tool, wiper
geometry etc. [1–4]. Wiper geometry is formed by three
curves to form a circular arc edge. The nose of wiper
provides less profile height on the surface, that is formed by the cutting edge, resulting in a smooth turning
surface. Insert with wiper has high efficiency when used
for finish and semi-finish turning. The surface quality
remains the same even at double feed rate [5]. Proper selection of these parameters, according to the properties
of workpiece material, reduces the cost of manufacturing
and the applied energy, lengthens the life of the cutting
tool and increases the surface quality of the manufactured product [4–8]. When all these are taken into consideration, it is obvious that the selection of the cutting
parameters in turning is very essential.
The machining of stainless steel inherently generates
high cutting temperature, which not only reduces tool
life but also impairs the workpiece surface quality [9, 10].
Obtaining the desired surface quality is very important
for the functional maintenance of a part [11–15]. One
of the stainless steel family materials, most commonly
used in the production facilities is steel with austenitic
structure. The austenitic stainless steels structure has
a combination of good mechanical properties and good
corrosion resistance [11].
In this study, an experimental investigation of cutting
forces and surface roughness after machining in turning
of AISI 304L stainless steel using wiper and conventional insert cutting tools are presented. The influences of
feed rate, depth of cut, corner radius and dry cutting
condition on the surface roughness and the cutting force
were examined. In order to optimize the turning process, Grey relational analysis Taguchi optimization method was used. The optimal machinability of AISI 304L
stainless steel with coated carbide insert was successfully
determined in this study.
2. Materials and equipment
The samples used in the experimental study had the
shape of a rod. Their length was 130 mm and diameter
was 25 mm. Chemical composition of AISI 304L stainless
steel is presented in Table I. A Johnford TC 35 CNC
Fanuc 0T CNC lathe was used.
TABLE I
Chemical composition AISI 304L stainless steel.
∗ e-mail:
C
0.04
muay@marmara.edu.tr
(349)
Mn
0.78
Cr
15.9
Mo
0.40
Ni
4.69
Co
0.06
Cu
3.4
350
Fig. 1.
M. Ay
Experimental setup.
TABLE II
Experimental factors and their levels.
Parameter
Level I
Level II
Level III
(A) Feed
[mm/rev]
0.1
0.2
0.3
(B) Depth
of cut [mm]
0.4
0.8
1.2
(C) Corner
radius [mm]
0.4
0.8
1.2
In the experimental study, Kennametal KC5010 PVD
TiAlN-coated conventional (FF) and wiper (FW) inserts
were used. The surface roughness value and hardness on
the work-piece obtained after the machining process were
measured by MAHR-Perth meter. Three measurements
were performed on the machined surfaces to determine
the Ra values. For the force measurements, Kistler 9121
force sensor, Kistler 5019b charge amplifier and DynoWare analysis program were used. Schematic drawing of
the experimental setup is given in Fig. 1.
For the experimental design Taguchi method was
employed.
" n
#
1X 2
S/N(η) = −10 log
y .
(1)
n i=1 i
Experimental factors and their levels were presented
in Table II for L9 experiment design. Results of surface
roughness and cutting force are shown in Table III.
TABLE III
Taguchi L9 experiment design, surface roughness and cutting force results.
Exp.
Variables
No.
1
2
3
4
5
6
7
8
9
A1 B1 C1
A1 B2 C2
A1 B3 C3
A2 B1 C2
A2 B2 C3
A2 B3 C1
A3 B1 C3
A3 B2 C1
A3 B3 C2
(A)
(B) (C)
Surface
Surface
Cutting Cutting Hardness Hardness
f
d
r
roughness roughness
force
force
(HRC)
(HRC)
[mm/rev] [mm] [mm] [µm] (FF) [µm] (FW) [N] (FF) [N] (FW)
(FF)
(FW)
1
1
1
0.72
1.04
255.80
338.76
28.6
27
1
2
2
0.76
0.77
353.74
463.05
22.87
26
1
3
3
0.54
0.64
389.15
589.21
21.27
24.8
2
1
2
1.41
0.9
287.37
497.50
29.47
26
2
2
3
1.24
0.81
348.45
567.30
23.23
26
2
3
1
2.01
1.26
382.33
638.43
26.9
24.3
3
1
3
1.13
0.84
278.33
494.16
27.63
26
3
2
1
2.50
1.43
386.48
555.86
25.93
25
3
3
2
2.13
0.97
487.38
676.09
23.63
28.73
351
Optimisation of Machining Parameters in Turning AISI 304L Stainless Steel. . .
2.1. Taguchi-based Grey relational analysis method
The obtained experimental results and the determined
parameters were optimized using Taguchi-based Grey
method. In regression model, research is carried out by
calculating an equation between dependent and independent parameters. The Taguchi method uses a special design of orthogonal arrays to study the entire parameter
space with a small number of experiments only.
Experimental design was done using Taguchi method.
Hence, it has been possible to reach more comprehensive results with doing less experiment. In this sense,
time and money have been used more efficiently [16, 17].
While a single outcome is optimized in the Taguchi method, multiple outcomes can be optimized in a Grey relational analysis [18]. In this study, Taguchi method was
used in the experimental design step, Grey relational analysis method was used in the optimization step.
Grey relational analysis optimization process was carried out in the following three steps [18].
1. Normalization of experimental results (the lowestthe best).
2. Calculation the Grey relational coefficient.
3. Calculation of the Grey relational degree.
4. Determination of optimal experiment parameters.
In the normalization step, the experimental results
were normalized using the following equation, according
to “the lowest – the best” principle.
max yi (k) − yi (k)
xi (k) =
,
(2)
max yi (k) − min yi (k)
where, xi (k) refers to the value at the ith series and kth
row after normalization process, min yi (k) refers to the
minimum value of the ith series, max yi (k) refers to the
maximum value of the ith series and yi (k) refers to the
original value at the ith series and kth row.
In the second step, Grey relational coefficient was calculated via Eq. (3)
∆ min +ζ∆ max
ξi (k) =
.
(3)
∆0i (k) + ζ∆ max
Here, ζ is a distinguishing coefficient between 0 and 1,
∆0i is the amount of deviation between the reference series and the normalization values, ∆ min refers to the
minimum value of the deviation sequence from the reference series and ∆ max refers to the maximum value of
deviation sequence from the reference series.
In step three, Grey relational degree was calculated
using Eq. (4)
n
1X
γi =
ξi (k).
(4)
n
k−1
3. Results and discussion
Influence of the cutting parameters and the effect of
cutting geometry and parameters on surface roughness
Ra and cutting force N on turning of a AISI 304L stainless steel with Kennametal KC5010 PVD TiAlN-coated
conventional (FF) and wiper (FW) inserts is discussed in
this section.
Grey relational analysis method was applied to the experimental results, as shown in Table III. The normalized
data, delta values and Grey relational grade results are
given in Tables IV and V.
TABLE IV
Normalized data, delta values and Grey relational grade for conventional insert tool.
Exp.
no.
1
2
3
4
5
6
7
8
9
Cutting Surface
Normalized data
Delta values
force roughness Hardness Cutting Surface
Cutting Surface
[N]
[µm]
(HRC)
force roughness Hardness force roughness Hardness
255.8
0.72
28.6
1.000
0.908
0.106
0.000
0.092
0.894
353.74
0.76
22.87
0.577
0.888
0.805
0.423
0.112
0.195
389.15
0.54
21.27
0.424
1.000
1.000
0.576
0.000
0.000
287.37
1.41
29.47
0.864
0.556
0.000
0.136
0.444
1.000
348.45
1.24
23.23
0.600
0.643
0.761
0.400
0.357
0.239
382.33
2.01
26.9
0.454
0.250
0.313
0.546
0.750
0.687
278.33
1.13
27.63
0.903
0.699
0.224
0.097
0.301
0.776
386.48
2.5
25.93
0.436
0.000
0.432
0.564
1.000
0.568
487.38
2.13
23.63
0.000
0.189
0.712
1.000
0.811
0.288
The Grey relational degrees, related to each
experiment result were calculated and the experimental results were ranked in order from highest Grey relational degree. Results are presented in Fig. 2 and
Table VI.
Grey relational
grade
Value Rank
0.735
1
0.679
2
0.732
3
0.658
4
0.569
5
0.439
6
0.731
7
0.402
8
0.357
9
As seen from the Table VI, A1 (feed of 0.1 mm/rev),
B1 (depth of cut of 0.4 mm), and C2 (corner radius
of 0.8 mm) were selected as the optimal parameters.
The optimal parameter values will provide the lowest surface roughness and cutting force values.
352
M. Ay
TABLE V
Normalized data, delta values and Grey relational grade for wiper insert tool.
Exp.
no.
1
2
3
4
5
6
7
8
9
Cutting Surface
Normalized data
Delta values
force roughness Hardness Cutting Surface
Cutting Surface
[N]
[µm]
(HRC)
force roughness Hardness force roughness Hardness
338.76
1.04
27
1.000
0.494
0.391
0.000
0.506
0.609
463.05
0.77
26
0.632
0.835
0.616
0.368
0.165
0.384
589.21
0.64
24.8
0.258
1.000
0.887
0.742
0.000
0.113
497.5
0.9
26
0.529
0.671
0.616
0.471
0.329
0.384
567.3
0.81
26
0.323
0.785
0.616
0.677
0.215
0.384
638.43
1.26
24.3
0.112
0.215
1.000
0.888
0.785
0.000
494.16
0.84
26
0.539
0.747
0.616
0.461
0.253
0.384
555.86
1.43
25
0.356
0.000
0.842
0.644
1.000
0.158
676.09
0.97
28.73
0.000
0.582
0.000
1.000
0.418
1.000
Grey relational
grade
Value Rank
0.649
1
0.664
2
0.701
3
0.559
4
0.562
5
0.375
6
0.592
7
0.385
8
0.439
9
• Based on the Grey relational analysis, the optimal cutting parameters were A1B1C2 for surface roughness and cutting force, i.e. feed of
0.1 mm/rev, depth of cut of 0.4 mm and corner
radius of 0.8 mm.
• Taguchi method is beneficial for the experimental
design of the machinability of AISI 304L stainless
steel alloy material. Having optimized parameters
is also fruitful for keeping the response values at
required levels.
Fig. 2.
Grey relational degrees for each experiment.
• The test results prove the effectiveness of the wiper
inserts in providing the excellent surface roughness.
The results also suggest that the use of the wiper
insert is an effective way to significantly increase
cutting efficiency, without changing the machined
surface roughness in high feed turning operations.
TABLE VI
Grey relational degrees of the factor levels for conventional insert tool.
Levels
Level 1
Level 2
Level 3
(A) Feed
[mm/rev]
1.301
0.830
0.686
(B) Depth
of cut [mm]
1.204
0.880
0.733
(C) Corner
radius [mm]
0.872
0.996
0.948
4. Conclusions
This study of the machinability of AISI 304L stainless steel alloy material with Kennametal KC5010 PVD
TiAlN-coated conventional (FF) and wiper (FW) inserts
has produced useful results. The criteria for the machinability are surface roughness, cutting force and material
hardness. Three control factors which were considered to
be effective in creating the most suitable conditions for
the criteria (feed rate, depth of cut and corner radius)
were chosen at three different levels and applied in the
experimental study. Below is the summary of the results:
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